Advanced Driver Assistance Systems (ADAS) mapping and/or navigation systems may use a point cloud of LIght Detection And Ranging (LIDAR) measurements in a 360 degrees horizontal pattern around the vehicle, which is correlated with a previously obtained high accuracy georeferenced texture map. As one example, many ADAS demand absolute accuracy levels in the range of 1 meter or less and relative accuracy (e.g. between two successive positions in a time period) in the decimeter range. Therefore, to maintain accuracy levels, the maps are often frequently updated.
With Global Navigation Satellite Systems (GNSS) based mapping and/or navigation systems, GNSS accuracy may degrade significantly in urban canyons, where multipath effects may induce an absolute position error of the order of tens of meters (e.g. as much as 50 meters) and relative position error of the order several meters. In addition, accuracy may be further degraded by the limited availability of good GNSS measurements. For example, with GNSS measurements that use carrier phase to achieve higher accuracy, positioning accuracy is dependent on a constant lock obtained by maintaining a clear view to at least four satellites, which may not be possible due to environmental conditions (e.g. in urban canyons). Further, accurate GNSS positioning also relies on the presence of a nearby reference receiver, which may not be available in many situations. In instances where accelerometer or IMU based inertial systems are used inertial sensor drift and other biases prevent reliable and accurate position determination.